Using EEG Neuro-Feedback technology to control a prosthetic hand
Unaffordable healthcare and excessive plastic waste are both alarming issues that are plaguing modern society. Recent studies conducted by the World Health Organisation (WHO) report that about 15% of the world's population suffer from a form of disability, of which 50% of the demographic cannot afford adequate health care. Furthermore, 8 million metric tons of plastic annually enter our oceans (apart from the 150 metric tons that currently circulate our oceans!). In conjunction to the global plastic pollution crisis, unnecessary invasive surgery is currently being done on amputees. Many of these desperate patients are forced to pay exorbitant prices in order to live a normal life with bionic prosthetics. The solution… Project Limbs - an EEG, 3D printed prosthetic printed from recycled plastic. Signal processors will be implemented to build an affordable and easy-to-use ‘mind controlled prosthetic hand’, that requires no invasive surgery.
Improving Particle Classification In Wimp Dark Matter Detection Using Neural Networks
In all experiments for detection of WIMP dark matter, it is essential to develop a classifier that can distinguish potential WIMP events from background radiation. Most often, clas- sifiers are developed manually, via physical modeling and empirical optimization. This is problematic for two reasons: it takes a great deal of time and effort away from developing the experiment, and the resulting classifiers often perform suboptimally (which means that a greater amount of expensive run time is required to obtain a confident experimental result). Machine learning has the potential to automate this and accelerate experimentation, and also to detect patterns that humans cannot. However, two major challenges, which are shared among several dark matter experiments, stand in the way: impure calibration data, which hinders training of models, and unpredictable physical dynamics within the detector itself. My objective was to develop a set of machine learning techniques that address these two problems, and thus more efficiently generate highly accurate classifiers. I was able to obtain raw data for two dark matter experiments which exhibit these challenges: the PICO-60 bubble chamber [2], and the DEAP-3600 liquid argon scintillator [1]. For each experiment, I developed and compared three general-purpose algorithms intended to resolve its inherent challenge (impurity and unpredictable dynamics, respectively). In PICO-60, background alpha and WIMP-like neutron calibration datasets are used for training; however, there is an impurity of 10% alphas in the neutron set. While a conventional classifier was developed (and is believed to be 100% accurate), machine learning in the form of a supervised neural network (NN) has also been previously explored, because of the benefits of automation. Unfortunately, it achieved a mean accuracy of only 80.2% – not usable as a practical replacement for conventional methods in future iterations of the experiment. In DEAP-3600, photons are absorbed by a wavelength shifting medium and re-emitted in an unpredictable direction, before being detected by one of 255 photomultiplier tubes (PMTs) around the spherical detector. The randomness severely limits the accuracy of conventional classifiers; in a simulation, the best so far removes 99.6% of alpha background, while also (undesirably) removing 91.0% of WIMP events. Because of physical limitations, simulated data is used for calibration, with 30 real-world experimental events available for testing. I have written a research paper [11] about my work on PICO-60, which has been approved by the PICO collaboration and pre-published at https://arxiv.org/abs/1811.11308. It is currently undergoing peer review for publication in Computer Physics Communications. All PICO researchers are listed on my paper for their work on the original PICO-60 experi- ment. They did not contribute to this study; I completed and documented it independently.
Chlorella vulgaris chlorophyll a fluorescence as a potential indicator for zinc and nickel detection
Heavy metals contaminate many bodies of water, posing a health risk to not only organisms that live and use the water in these areas, but also to the humans that live nearby. Chlorella vulgaris, a microalga, is one organism whose chlorophyll a fluorescence can indicate the presence of these substances, detecting any changes in concentrations using fluorescence microscopy and other fluorescence devices. The study explores the sensitivity of C. vulgaris to the heavy metal zinc where the algae was exposed to five concentrations of zinc: 0 ppm, 5 ppm, 10 ppm, 50 ppm, and 100 ppm. The fluorescence of the samples was observed with a fluorescence microscope on days 0, 4, 7, and 12, where the algal samples were adapted to the dark for 5 minutes, then exposed to light for 90 seconds. The values of the minimal and maximal fluorescence of the samples in the dark were noted. There is a significant difference in the values of the minimal fluorescence, maximal fluorescence, and maximum quantum yield, a value derived from the minimal and maximal fluorescence, at the highest concentration, 100 ppm, from the other treatments for the entirety of the experiment. The significantly low values at 100 ppm and the calculated EC50 of 75.70 ppm indicate that C. vulgaris is indeed a viable indicator for zinc detection at this and higher concentrations of zinc.
Removal of Nutrients by Chlorella Vulgaris Microalgae in Bandar Abbas Municipal Wastewater
The entry of nutrients into the environment can cause the creation of eutrophication of aquatic ecosystems. One of the methods of removing nutrients from effluents is the use of algae. Algal purification is a new and inexpensive technology for this purpose. The present study investigated the rate of cell growth and nutrient removal of urban wastewater in Bandar Abbas in winter 2020 by the Chlorella vulgaris microalgae in the phycolab of Fisheries Research. Treatments with different dilutions (0%, 25%, 50% and 75%) were prepared; in addition, specific growth rate, cell density and removal efficiency of phosphate, nitrate, nitrite were examined during a 14 day period with initial constant density (1×10⁶ cells / ml ) of microalgae. The results indicated that 0% and 75% dilution had the highest and lowest cell densities (8.675×10⁶ and 56.633×10⁶), respectively; moreover, they had the specific growth rate (0.166 and 0.311). Furthermore, there was a significant difference between them (P≥ 0.05). The highest nitrate and nitrite removal efficiencies were -40.75 and -79.84 in effluent dilution of 50%; in addition, the lowest were 1.26 and -40.26 in dilution of 75% and 25% respectively. Phosphate had the highest removal efficiency at 0% dilution with a mean of -79.65 that showed a significant difference with the lowest at 25% dilution (P≥ 0.05). Therefore, high or low levels of nutrients can affect the removal efficiency and growth rate of microalgae.